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A query containing a WHERE clause is running longer than expected. The Query Profile shows that all micro-partitions being scanned How should this query be optimized?
Which minimum Snowflake edition allows for a dedicated metadata store?
How can the outer array structure of a semi-structured file be removed?
Who can access the data published in a Data Exchange?
What are characteristics of the ownership privilege when it is granted on a regular Snowflake schema? (Select TWO).
What virtual warehouse configuration should be used when processing a large number of complex queries?
While preparing to unload data in Snowflake, the file format option can be specified in which commands? (Select TWO).
Which Query Profile metrics will provide information that can be used to improve query performance? (Select TWO).
How can a data provider validate that a secure view is configured to display only the data the provider wishes to expose?
How does the search optimization service improve query performance?

Question 381

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How should clustering be used to optimize the performance of queries that run on a very large table?

A.
Manually re-cluster the table regularly.
A.
Manually re-cluster the table regularly.
Answers
B.
Choose one high cardinality column as the clustering key.
B.
Choose one high cardinality column as the clustering key.
Answers
C.
Use the column that is most-frequently used in query select clauses as the clustering key.
C.
Use the column that is most-frequently used in query select clauses as the clustering key.
Answers
D.
Assess the average table depth to identify how clustering is impacting the query.
D.
Assess the average table depth to identify how clustering is impacting the query.
Answers
Suggested answer: B

Explanation:

For optimizing the performance of queries that run on a very large table, it is recommended to choose one high cardinality column as the clustering key.This helps to co-locate similar rows in the same micro-partitions, improving scan efficiency in queries by skipping data that does not match filtering predicates4.

References:[COF-C02] SnowPro Core Certification Exam Study Guide

Question 382

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What step can reduce data spilling in Snowflake?

A.
Using a larger virtual warehouse
A.
Using a larger virtual warehouse
Answers
B.
Increasing the virtual warehouse maximum timeout limit
B.
Increasing the virtual warehouse maximum timeout limit
Answers
C.
Increasing the amount of remote storage for the virtual warehouse
C.
Increasing the amount of remote storage for the virtual warehouse
Answers
D.
Using a common table expression (CTE) instead of a temporary table
D.
Using a common table expression (CTE) instead of a temporary table
Answers
Suggested answer: A

Explanation:

To reduce data spilling in Snowflake, using a larger virtual warehouse is effective because it provides more memory and local disk space, which can accommodate larger data operations and minimize the need to spill data to disk or remote storage1. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 383

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Which VALIDATION_MODE value will return the errors across the files specified in a COPY command, including files that were partially loaded during an earlier load?

A.
RETURN_-1_R0WS
A.
RETURN_-1_R0WS
Answers
B.
RETURN_n_ROWS
B.
RETURN_n_ROWS
Answers
C.
RETURN_ERRORS
C.
RETURN_ERRORS
Answers
D.
RETURN ALL ERRORS
D.
RETURN ALL ERRORS
Answers
Suggested answer: C

Explanation:

TheRETURN_ERRORSvalue in theVALIDATION_MODEoption of the COPY command instructs Snowflake to validate the data files and return errors encountered across all specified files, including those that were partially loaded during an earlier load2. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 384

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Which solution improves the performance of point lookup queries that return a small number of rows from large tables using highly selective filters?

A.
Automatic clustering
A.
Automatic clustering
Answers
B.
Materialized views
B.
Materialized views
Answers
C.
Query acceleration service
C.
Query acceleration service
Answers
D.
Search optimization service
D.
Search optimization service
Answers
Suggested answer: D

Explanation:

The search optimization service improves the performance of point lookup queries on large tables by using selective filters to quickly return a small number of rows.It creates an optimized data structure that helps in pruning the micro-partitions that do not contain the queried values3. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 385

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Which Snowflake object does not consume any storage costs?

A.
Secure view
A.
Secure view
Answers
B.
Materialized view
B.
Materialized view
Answers
C.
Temporary table
C.
Temporary table
Answers
D.
Transient table
D.
Transient table
Answers
Suggested answer: C

Explanation:

Temporary tables do not consume any storage costs in Snowflake because they only exist for the duration of the session that created them and are automatically dropped when the session ends, thus incurring no long-term storage charges4. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 386

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Which function unloads data from a relational table to JSON?

A.
TO_OBJECT
A.
TO_OBJECT
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B.
TO_JSON
B.
TO_JSON
Answers
C.
TO_VARIANT
C.
TO_VARIANT
Answers
D.
OBJECT CONSTRUCT
D.
OBJECT CONSTRUCT
Answers
Suggested answer: B

Explanation:

TheTO_JSONfunction is used to convert a VARIANT value into a string containing the JSON representation of the value. This function is suitable for unloading data from a relational table to JSON format. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 387

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Which commands can only be executed using SnowSQL? (Select TWO).

A.
COPY INTO
A.
COPY INTO
Answers
B.
GET
B.
GET
Answers
C.
LIST
C.
LIST
Answers
D.
PUT
D.
PUT
Answers
E.
REMOVE
E.
REMOVE
Answers
Suggested answer: C, D

Explanation:

TheLISTandPUTcommands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients.LISTis used to display the contents of a stage, andPUTis used to upload files to a stage. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 388

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What is the relationship between a Query Profile and a virtual warehouse?

A.
A Query Profile can help users right-size virtual warehouses.
A.
A Query Profile can help users right-size virtual warehouses.
Answers
B.
A Query Profile defines the hardware specifications of the virtual warehouse.
B.
A Query Profile defines the hardware specifications of the virtual warehouse.
Answers
C.
A Query Profile can help determine the number of virtual warehouses available.
C.
A Query Profile can help determine the number of virtual warehouses available.
Answers
D.
A Query Profile automatically scales the virtual warehouse based on the query complexity.
D.
A Query Profile automatically scales the virtual warehouse based on the query complexity.
Answers
Suggested answer: A

Explanation:

A Query Profile provides detailed execution information for a query, which can be used to analyze the performance and behavior of queries. This information can help users optimize and right-size their virtual warehouses for better efficiency. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 389

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What is the primary purpose of a directory table in Snowflake?

A.
To store actual data from external stages
A.
To store actual data from external stages
Answers
B.
To automatically expire file URLs for security
B.
To automatically expire file URLs for security
Answers
C.
To manage user privileges and access control
C.
To manage user privileges and access control
Answers
D.
To store file-level metadata about data files in a stage
D.
To store file-level metadata about data files in a stage
Answers
Suggested answer: D

Explanation:

A directory table in Snowflake is used to store file-level metadata about the data files in a stage. It is conceptually similar to an external table and provides information such as file size, last modified timestamp, and file URL. References: [COF-C02] SnowPro Core Certification Exam Study Guide

Question 390

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Which statements describe benefits of Snowflake's separation of compute and storage? (Select TWO).

A.
The separation allows independent scaling of computing resources.
A.
The separation allows independent scaling of computing resources.
Answers
B.
The separation ensures consistent data encryption across all virtual data warehouses.
B.
The separation ensures consistent data encryption across all virtual data warehouses.
Answers
C.
The separation supports automatic conversion of semi-structured data into structured data for advanced data analysis.
C.
The separation supports automatic conversion of semi-structured data into structured data for advanced data analysis.
Answers
D.
Storage volume growth and compute usage growth can be tightly coupled.
D.
Storage volume growth and compute usage growth can be tightly coupled.
Answers
E.
Compute can be scaled up or down without the requirement to add more storage.
E.
Compute can be scaled up or down without the requirement to add more storage.
Answers
Suggested answer: A, E

Explanation:

Snowflake's architecture allows for the independent scaling of compute resources, meaning you can increase or decrease the computational power as needed without affecting storage. This separation also means that storage can grow independently of compute usage, allowing for more flexible and cost-effective data management.

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